Mixtures of Metals and Polycyclic Aromatic Hydrocarbons Lead to Complex Toxic Outcomes in the Freshwater Amphipod, Hyalella azteca
Bibliographic record
Abstract
The study of ecotoxicology inevitably must address the fact that toxicants always occur in mixture. \nUnfortunately, our present state of knowledge in terms of predicting the effects of contaminant \nmixtures and understanding the mechanisms by which a mixture can produce non-additive (e.g., antagonistic or synergistic) toxicity is insufficient to advise regulatory authorities on appropriate water quality objectives for the protection of aquatic life. Metals and polycyclic aromatic hydrocarbons (PAHs) are two ubiquitous contaminants that are often associated with similar effluent sources, such as bitumen and municipal waste. The adverse toxicological effects that metals and PAHs produce have given these contaminants the status of ?priority pollutants? in many countries, including the USA and Canada. Thus, understanding their toxicological effects when in mixture should also be a priority. However, to date, there have been only 11 studies that have investigated the potential for metals and PAHs to produce non-additive toxicity. Of these 11 studies, reports of more-than-additive lethality have been equally common as strictly-additive lethality, raising concern \nover the largely ignored ecological risk these contaminants types produce when in mixture. \nThe research outlined in this dissertation expands our understanding by providing the first \ncomprehensive review of mechanistic aspects of metal-PAH mixture toxicity that likely amount to \nmore-than-additive co-toxicity. This dissertation outlines experimental work investigating the \nadditivity of binary mixtures of Cu, Cd, Ni, and V, with either phenanthrene (PHE) or phenanthrenequinone (PHQ), two common PAHs. For cases where more-than-additive mortality was found, additional experimentation was carried out to explore interactive toxic mechanisms in attempt to explain why certain mixtures of metals and PAHs produce more-than-additive lethality. Finally, the effects of Cu, PHE, and Cu-PHE mixtures were studied in terms of their effects on behaviour, a sublethal endpoint that mediates ecological effects which can also be used to predict toxic mechanisms. All experimental work outlined in this dissertation involved the aquatic crustacean amphipod, Hyalella azteca, which was selected due \nto its tractability in a laboratory setting, its ecological importance as a food source for fish, amphibians, and waterfowl, and its widespread distribution throughout North and Central America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".